基于最优光谱指数的大豆叶片叶绿素含量反演模型研究

叶绿素含量的准确获取及预测可为作物种植的精准化管理提供理论依据.利用最优光谱指数建立大豆叶绿素含量反演模型,以大豆花芽分化期叶片为研究对象,获取高光谱和叶绿素含量数据.首先构建了7种与叶绿素含量相关的典型光谱指数,分别为比值指数(RI)、差值指数(DI)、归一化差值植被指数(ND-VI)、修正简单比值指数(mSR)、修...

Study on Inversion Model of Chlorophyll Content in Soybean Leaf Based on Optimal Spectral Indices

The accurate acquisition and prediction of chlorophyll content can provide a theoretical basis for precise management of crop planting. Optimal spectral index was used to establish the soybean chlorophyll content inversion model in this paper. The hyperspectral and chlorophyll content data of soybean flower bud differentiation were obtained. Firstly, seven typical spectral indices related to chlorophyll content were constructed, namely ratio index (RI), difference index (DI), normalized difference vegetation index (NDVI), modified simple ratio index (mSR), modified normalized difference index (mNDI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), respectively. First derivative (FD) processing was performed on the original hyper spectrum, and then the original and first derivative hyper spectrum are combined with all wavelengths in the full spectrum wavelength range to calculate 14 spectral indices. Then use the correlation matrix method to select the optimal spectral index. The correlation analysis was conducted between the spectral index calculated by all wavelength combinations and chlorophyll content. The maximum value of the correlation coefficient was taken as the index to extract the 14 optimal wavelength combinations, and the corresponding spectral index value was calculated as the optimal spectral index. Finally, the optimal spectral indices were divided into three groups as model input variables combined with the three methods of Partial least squares regression (PLS), Least squares support vector machine regression (LSSVM), and LASSO regression to model, then compare and analyze the results. The coefficients of determination R2_c, R2_p and the root mean square error RMSEC and RMSEP as model evaluation indicators, then soybean chlorophyll content inversion model with the highest accuracy, were finally selected. The results show that the 14 optimal spectral index wavelength combinations are RI (728, 727), DI (735, 732), NDVI (728, 727), mSR (728, 727), mNDI (728, 727), SAVI (728, 727), TVI (1 007, 708), FDRI (727, 708), FDDI (727, 788), FDNDVI (726, 705), FDmSR (726, 705), FDmNDI (726, 705), FDSAVI (727, 788) and FDTVI (760, 698), the maximum correlation coefficient with chlorophyll content are all greater than 0.8. The method to establish the optimal chlorophyll inversion model was the LSSVM modeling method combined with the first derivative spectral index (combination 2). The R2_c=0.751 8, R2_p=0.836 0, RMSEC=1.361 2, RMSEP=1.220 4, indicating that the model had high accuracy and could provide a reference for monitoring the growth status of soybean in a large area.